Maximum likelihood fitting of two distributions and goodness-of-fit
comparison.

Different distributions may be used depending on the kind of provided data.
By default, the Poisson and negative binomial distributions are fitted to
count data, whereas the binomial and beta-binomial distributions are used
with incidence data. Either Randomness assumption (Poisson or binomial
distributions) or aggregation assumption (negative binomial or beta-binomial)
are made, and then, a goodness-of-fit comparison of both distributions is
made using a log-likelihood ratio test.

# Note that there are other methods to fit some common distributions.# For example for the Poisson distribution, one can use glm:my_arthropods<-arthropods[arthropods$t==3, ]
my_model<-glm(my_arthropods$i ~ 1, family=poisson)
lambda<-exp(coef(my_model)[[1]]) # unique(my_model$fitted.values) works also.lambda